Spectral imaging of biological samples

ABSTRACT

The invention features a method including: (i) providing spectrally resolved information about light coming from different spatial locations in a sample comprising deep tissue in response to an illumination of the sample, wherein the light includes contributions from different components in the sample; (ii) decomposing the spectrally resolved information for each of at least some of the different spatial locations into contributions from spectral estimates associated with at least some of the components in the sample; and (iii) constructing a deep tissue image of the sample based on the decomposition to preferentially show a selected one of the components.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority under 35U.S.C. § 120 to U.S. application Ser. No. 10/573,242, filed Mar. 22,2006, which claims priority to WIPO Application Serial No.PCT/2004/031609, filed Sep. 23, 2004, which claims priority to U.S.application Ser. No. 10/669,101, filed on Sep. 23, 2003. The contents ofthe prior applications are incorporated herein by reference in theirentirety.

BACKGROUND

Optical imaging of deep tissue is used to probe structures withinbiological specimens for laboratory research and biomedical purposes.This includes the imaging of internal organs and subdermal tissue inanimals such as mice, zebrafish, or humans, and one of the goals is tolearn about internal structures without surgery or other intrusivemeasures.

In one technique of deep tissue imaging, fluorescent agents which areassociated with a specific target in the specimen are imaged by excitingthem with illumination light, causing them to fluoresce; the fluorescentemission is separated from the illumination light, which has a differentwavelength, by barrier filters and then is detected using a verysensitive camera such as a cooled CCD detector. In other techniques, thespecimen is modified using agents that cause it to produce material thatis inherently fluorescent, with the most common example being greenfluorescent protein (GFP). Further techniques involve use of quantumdots as luminous probes.

As used herein, compounds such as fluorescent dyes, fluorescent proteinssuch as GFP, quantum dots, surface-enhanced Raman reagents, as well asrelated compounds or others used for similar purposes, are all examplesof a “target compound” of a measurement.

The signals produced in such experiments are typically weak. In general,robust detection of the weak levels of light emitted from the deepstructures is beneficial because it provides earlier, or more reliable,detection of the structures being studied. Also, it may enable detectionof lower levels of the target compound. Accordingly, techniques orapparatus used for deep tissue imaging are valued if they offer a lowdetection threshold.

SUMMARY

The inventors have recognized that one can successfully use spectraldiscrimination techniques to accurately image one or more targetcompounds in a deep tissue sample, such as a subdermal tissue or anorgan in an animal or a human. For example, one collects spectrallyresolved information about light coming from different spatial locationsin a sample, and then decomposes the spectrally resolved informationinto contributions from estimates of the pure spectra corresponding todifferent components in the sample (e.g., autofluorescence and one ormore target compounds). This decomposition can be used to reconstructone or more images that preferentially show a selected component. Thespectrally resolved information typically corresponds to a spectralimage cube in which each pixel includes a sample emission spectrumcoming from a corresponding spatial location.

The inventors have also developed algorithms that are particularlyuseful for estimating the pure spectrum of one or more of the samplecomponents from such spectral image cube data, even where one or more ofthe components are only present in a mixed form (i.e., light from onecomponents overlaps both spatially and spectrally with light fromanother component). These pure spectra can then be used in thedecomposition of the spectral image cube data into selected componentimages. These algorithms are useful not only for deep tissue samples,but for biological samples in general. Moreover, in some embodiments thealgorithms require little or no user input.

In some embodiments, the algorithms recognize that an accurate estimatefor the pure spectrum of a first component that is only present in theimage cube in mixed form can be determined by using at least part of theimage cube data and a separate estimate for the pure spectrum of atleast a second component present in the mixture. The estimate of thepure spectrum for the second component can be based on other parts ofthe image cube data or from prior knowledge. In the simplest example, ascaled amount of the spectral estimate for the second component issubtracted from the mixed signal spectrum to reveal an estimate for thepure spectrum of the first component. For example, the scaling can beset to acheive small, but non-zero signal values in each spectralchannel.

We now generally summarize at least some of the different aspects andfeatures of the invention.

In general, in one aspect, the invention features a method including:(i) providing spectrally resolved information about light coming fromdifferent spatial locations in a sample (e.g., a deep tissue sample) inresponse to an illumination of the sample, wherein the light includescontributions from different components in the sample; and (ii)constructing an image of the sample based on the spectrally resolvedinformation to preferentially show a selected one of the components.Typically, the spectrally resolved information includes informationcorresponding to at least three, and preferably four or more, differentspectral weighting functions (e.g., different spectral bands).

The method may further include decomposing the spectrally resolvedinformation for each of at least some of the different spatial locationsinto contributions from a spectral estimate associated with each of atleast one or more of the components in the sample. The construction ofthe image may be based on this decomposition. Also, one or more of thespectral estimates may be estimates of the pure spectra for thecomponents. The pure spectrum of a given component corresponds to thespectrally resolved information that would be observed if only thatcomponent contributes to the light being measured (for a given spatiallocation).

The method may further include any of the following features.

The spectrally resolved information may include information about a setof images in which the light coming from the sample is spectrallyfiltered, wherein the spectral filtering for each image corresponds to adifferent spectral weighting function.

The spectrally resolved information may include information about a setof images in which light used to illuminate the sample is spectrallyfiltered, wherein the spectral filtering for each image corresponds to adifferent spectral weighting function.

The different spatial locations typically correspond to common pixels inthe set of images. The different spectral weighting functions cancorrespond to different spectral bands. There may three, or morepreferably, four or more, images in the set of images.

The information about the set of images may include a series of valuesat each of the pixels, wherein each value is related to an intensity ofthe light coming from the sample with respect to a corresponding one ofthe spectral weighting functions. The spectrally resolved informationfor each spatial location typically includes information correspondingto at least three, and more preferably, four or more different spectralweighting functions.

The spectrally resolved information may include a spectral image cube.

The light coming from the sample may include fluorescence from thesample, reflectance, phosphorescence, scattering, or Raman scatteringfrom the sample, or it may include light transmitted through the sample.

At least one of the components may relate to autofluorescence.

At least one of the components may include a target compound (e.g., afluorescent protein or a quantum dot). For example, the selectedcomponent may be the component including the target compound.

The method may further include illuminating the sample and collectingthe spectrally resolved information. For example, collecting thespectrally resolved information may include using a liquid crystaltunable spectral filter, an acousto-optical tunable spectral filter, aset of spectral filters, a spectral filter wheel, a dispersive prism, agrating, a spectrometer, or monochromator.

The image of the selected component may includes an image in whichsignal from the other components is reduced relative to signal from theselected component.

The method may further include constructing a second image of the samplebased on the decomposition to preferentially show a second one of thecomponents. The method may also include constructing a third image ofthe sample based on the decomposition to preferentially show a third oneof the components.

The sample may be a living organism (e.g., a mammal).

The sample may include deep tissue, tissue slices, cells, subdermaltissue, or a microscope slide carrying biological material.

Constructing the image based on the decomposition may includeconstructing the deep tissue image based on the contributions atdifferent spatial locations of the spectral estimate associated with theselected component.

The decomposition may be a linear decomposition. For example, thedecomposition may include solving at least one component of a matrixequation in which one matrix in the equation is based on the spectrallyresolved information and another matrix in the equation is based on thespectral estimates.

At least one of the spectral estimates may be provided independently ofthe spectrally resolved information.

At least a first one of the spectral estimates for a first one of thecomponents may be determined from the spectrally resolved information.For example, all of the spectral estimates may be determined from thespectrally resolved information. The spectral estimates may bedetermined from the spectrally resolved information by using anunsupervised classification technique or a supervised classificationtechnique. One example of an unsupervised classification techniqueincludes averaging the spectrally resolved information for multiple onesof the spatial locations. One example of a supervised classificationtechnique determining the first spectral estimate from the spectrallyresolved information a region including one or more of the spatiallocations, wherein the region is associated with the first component.

The first spectral estimate may be derived from the spectrally resolvedinformation from a first set of one or more spatial locations in whichthe light includes contributions from multiple ones of the components.In such case, the first spectral estimate can be derived from thespectrally resolved information from the first set of spatial locationsand a spectral estimates for a second one of the components.

For example, deriving the first spectral estimate may includecalculating a remainder spectrum based on the spectrally resolvedinformation from the first set and the spectral estimate for the secondcomponent. The remainder spectrum can be calculated at each of one ormore of the spatial locations in the first set of spatial locations.Alternatively, the remainder spectrum can be calculated based on anaverage of the spectrally resolved information in the first set ofspatial locations and the spectral estimate for the second component.

The spectral estimate for the second component may be derived from thespectrally resolved information. For example, the spectral estimate forthe second component may be determined from the spectrally resolvedinformation by using an unsupervised classification technique, such asaveraging. Alternatively, the spectral estimate for the second componentmay be derived from a region including one or more of the spatiallocations, wherein the region is associated with the second component.

Deriving the first spectral estimate may includes adjusting valuescorresponding to the spectrally resolved information for the first setof spatial locations to remove a contribution (e.g., a maximalcontribution) from the second component based on the spectral estimatefor the second component. The maximal contribution may be based on anerror function analysis of signal in each spectral channel of theadjusted values. For example, the error function analysis tends tomaintain nonnegative signal in each spectral channel of the adjustedvalues.

For example, the values may include a series of at least some of thevalues for each of the spatial locations in the first set, and removingthe contribution from the second component based on the spectralestimate for the second component may include subtracting an optimizedquantity of the spectral estimate for the second component from each ofthe series of values.

The optimized quantity for at least a first of the series of values maybe based on minimizing an error function of a difference spectrum thatincludes a difference between the first series values and the quantityto be optimized multiplied by the spectral estimate for the secondcomponent, wherein the error function is minimized over the spectralchannels. The difference spectrum may further include a constant that isalso optimized over the spectral channels. The error function typicallyfavors positive values of the difference spectrum over negative valuesof the difference spectrum. For example, one useful error function isincludes (e^(−Δ)+1)Δ², where Δ is the difference spectrum. The errorfunction may also be normalized by the magnitudes of the first series ofvalues and the spectral estimate for the second component.

The decomposition may include: (i) a first decomposition of thespectrally resolved information at multiple spatial locations intocontributions from initial spectral estimates associated with at leastsome of the components in the sample; (ii) improving an accuracy of atleast some of the initial spectral estimates based on the firstdecomposition; and (iii) at least a second decomposition of thespectrally resolved information at multiple spatial locations intocontributions from the improved spectral estimates.

In another aspect, the invention features an apparatus including: (i) asample holder configured to support a sample (e.g., a deep tissuesample); (ii) an illumination source to illuminate the sample; adetector positioned to detect light from the sample; and (iii) anelectronic processor coupled to the detector. The electronic processoris configured to implement any of the method steps described above,including interacting with a user as necessary.

The apparatus may further include a spectral filtering means positionedbetween the sample and the detector. For example, the spectral filteringmeans may include a liquid crystal tunable spectral filter, anacousto-optical tunable spectral filter, a set of spectral filters, aspectral filter wheel, a dispersive prism, a grating, a spectrometer, ormonochromator.

Alternatively, or in addition, the apparatus may include a spectralfiltering means positioned between the illumination source and thesample.

Also, the illumination source itself may provide tunable excitationlight. For example, it may be a multispectral diode or LED array.

In general, in yet a further aspect, the invention features a methodincluding: (i) providing a set of images of spectrally filteredradiation emitted from a biological sample in response to anillumination, wherein the sample includes a component supporting atarget compound, the emitted radiation includes emission from the targetcompound and emission from one or more additional components in thesample, and each image corresponds to a different spectral weightingfunction for a common set of pixels; and (ii) processing the images ofthe spectrally filtered radiation to construct an output image of thesample in which signal from the additional components is reducedrelative to signal from the target compound. The processing includescalculating a remainder spectrum for one or more pixels in the set ofimages based on an estimate for an emission spectrum of at least one ofthe components.

In yet another aspect, the invention features a method including: (i)illuminating a sample to cause the sample to emit radiation, wherein thesample includes deep tissue supporting a target compound, and whereinthe emitted radiation includes emission from the target compound andemission from one or more other components in the sample; (ii)spectrally filtering the emitted radiation with each of a plurality ofdifferent spectral weighting functions; (iii) storing an image of thespectrally filtered radiation for each of the spectral weightingfunctions; and (iv) processing the stored images to construct a deeptissue image of the sample in which signal from the other compounds isreduced relative to signal from the target compound.

In another aspect, the invention features a method including: (i)providing a plurality of images of spectrally filtered radiation emittedfrom a sample in response to an illumination, wherein the sampleincludes deep tissue supporting a target compound, wherein the emittedradiation includes emission from the target compound and emission fromone or more other components in the sample, and wherein each imagecorresponds to a different spectral weighting function; and (ii)processing the images of the spectrally filtered radiation to constructa deep tissue image of the sample in which signal from the othercompounds is reduced relative to signal from the target compound.

In yet another aspect, the invention features an apparatus including acomputer readable medium which stores a program that causes a processorto: (i) receive a plurality of images of spectrally filtered radiationemitted from a sample in response to an illumination, wherein the sampleincludes deep tissue supporting a target compound, wherein the emittedradiation includes emission from the target compound and emission fromone or more other components in the sample, and wherein each imagecorresponds to a different spectral weighting function; and (ii) processthe images of the spectrally filtered radiation to construct a deeptissue image of the sample in which signal from the other compounds isreduced relative to signal from the target compound.

In yet another aspect, the invention features an apparatus comprising:(i) a sample holder configured to hold a sample including deep tissue,wherein the deep tissue supports a target compound; (ii) an illuminationsource configured to illuminate the sample to cause it to emitradiation, wherein the emitted radiation includes emission from thetarget compound and emission from one or more other components in thesample; (iii) an imaging system configured to image the emittedradiation to a detector; (iv) a tunable spectral filter configured tospectrally filter the emitted radiation with each of a plurality ofdifferent spectral weighting functions; (v) a detector configured tostore an image of the spectrally filtered radiation for each of thespectral weighting functions; and (vi) a electronic processor configuredto process the store images to construct a deep tissue image of thesample in which signal from the other compounds is reduced relative tosignal from the target compound. For example, the sample holder mayconfigured to hold an animal, such as a mammal, like a mouse, rabbit, orhuman. Also, for example, the imaging system may have a demagnificationgreater than or equal to 1, and, for example, the imaging system may beconfigured to image a field of view having a diagonal dimension greaterthan about 2 cm onto the detector.

Embodiments of these various aspects may include any of the followingfeatures.

The sample including the deep tissue may be a living organism, such asan animal or a mammal. For example, the animal may include a mouse, arabbit, a zebrafish, or a human. Also, the deep tissue may be aninternal organ of the living organism, and the deep tissue may liewithin about 2 mm or more of the living organism.

The deep tissue may be subdermal tissue.

The emission from the other components of the sample may includeautofluorescence from tissue overlying the deep tissue.

The emission from the other components of the sample may includeautofluorescence from one or more layers of tissue in the sampledifferent from a layer of tissue including the deep tissue.

The target compound may be any of, for example, a fluorescent probebound to at least a portion of the deep tissue, a quantum dot bound toat least a portion of the deep tissue, a green fluorescent protein (GFP)bound to at least a portion of the deep tissue, a yellow fluorescentprotein (YFP) bound to at least a portion of the deep tissue, and a redfluorescent protein (RFP) bound to at least a portion of the deeptissue.

The emission from the target compound may be fluorescence.

At least some of the spectral weighting functions may correspond toparticular wavelength bands. For example, all of the spectral weightingfunctions correspond to particular wavelength bands. Alternatively, atleast some of the spectral weighting functions may correspond tosinusoidal weightings of multiple wavelength bands.

The spectral filtering may include using any of a liquid-crystal,tunable optical filter, an interferometric optical filter, and a filterwheel containing a plurality of band pass filters.

Each stored image may include an intensity value for each of multiplepixels.

Processing the stored images may include constructing the deep tissueimage based on a weighted superposition of signals in the stored images.

Processing the recorded images may include constructing the deep tissueimage based on the recorded images and at least one emission spectrumfor the other components in the sample. For example, constructing thedeep tissue image may include calculating a remainder spectrum for eachpixel in the set of stored images based on the at least one emissionspectrum for the other components.

Similarly, processing the recorded images may include constructing thedeep tissue image based on the recorded images and an emission spectrumfor the target compound. For example, constructing the deep tissue imagemay include calculating a remainder spectrum for each pixel in the setof stored images based on the emission spectrum for the target compound.

Also, processing the recorded images may include constructing the deeptissue image based on the recorded images, at least one emissionspectrum for the other components in the sample, and an emissionspectrum for the target compound. For example, constructing the deeptissue image may include solving at least one component of a matrixequation in which one matrix is based on the stored images, and anothermatrix is based on the emission spectra.

The deep tissue may support multiple target compounds and processing thestored images may include constructing a deep tissue image for each ofthe target compounds. For example, processing the recorded images mayinclude constructing the deep tissue images based on the recorded imagesand emission spectra for the target compounds. Furthermore, processingthe recorded images may include constructing the deep tissue imagesbased on the recorded images, the emission spectra for the targetcompounds, and at least one emission spectrum for the other componentsin the sample.

The plurality of the different spectral weighting functions may includefour or more spectral weighting functions.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. All publications, patentapplications, patents, and other references mentioned herein areincorporated by reference in their entirety. In case of conflict, thepresent specification, including definitions, will control.

Other features, objects, and advantages of the invention will beapparent from the following detailed description.

DESCRIPTION OF DRAWINGS

The invention will now be further described merely by way of examplewith reference to the accompanying drawings.

FIG. 1 is a schematic diagram of a spectral imaging system.

FIG. 2 is an image of a mouse which has been implanted with afluorescently labeled tumor, imaged at a single spectral band of λ=530nm.

FIG. 3 is a graph of the emission spectrum of autofluorescence and theestimated emission spectrum of the target compound for the sample ofFIG. 2, with the light-colored line showing the spectrum for theautofluorescence and the dark-colored line showing the estimatedspectrum for the target compound.

FIGS. 4A and 4B are images showing the results of spectral unmixingoperations that used as spectral end-members the measured mouseautofluorescence signal spectrum and the mixed signal spectra detectedover the region of the tumor, respectively.

FIG. 5 is a set of images forming an image cube.

FIG. 6 is an image of the target compound emissions from the mouse ofFIG. 2, with the autofluorescence signal removed using spectraltechniques and end-member spectra that are estimated as well as directlymeasured.

FIG. 7 is an image of the autofluorescence emissions from the mouse ofFIG. 2, with the target compound emissions separated using spectraltechniques and end-member spectra that are estimated as well as directlymeasured.

FIG. 8 is a flow-chart of one embodiment, based on acquisition of aspectral cube and subsequent analysis by linear unmixing.

FIG. 9 is graph of the spectral properties of each spectral band in theembodiment of FIG. 8.

FIG. 10 is a flow-chart of yet another embodiment in which the spectralfiltering is performed interferometrically.

FIG. 11 is a graph of the spectral properties of the spectral weightingsused in the embodiment of FIG. 10. Like reference symbols in the variousdrawings indicate like elements.

FIG. 12 is a flow-chart of another embodiment which incorporatesspectral component decomposition to estimate the spectra of theautofluorescence and the target compound(s).

FIGS. 13A, 13B and 13C are exemplary images component abundance imagesgenerated by a typical non-life sciences end-member detection algorithm.

FIGS. 14A, 14B and 14C are output images generated by an unsupervisediterative remainder technique.

FIGS. 15A and 15B are output images generated by a supervised iterativeremainder technique.

FIG. 16 is a flow-chart for a process for extracting spectral vectors.

FIG. 17 is a schematic diagram of a spectral imaging system.

DETAILED DESCRIPTION Overview

The invention features methods and apparatus for reducing the detectionsensitivity level of a target compound in a biological sample (includingdeep tissue in the sample) through spectral discrimination. Thisinvolves measuring spectrally resolved information about the lightcoming from each of multiple spatial locations in the sample. Sometimes,the data is referred to as an “image cube,” with the spectrally resolvedinformation corresponding to values along one dimension of the cube andthe spatial locations (or pixels) corresponding to the other twodimensions of the cube. Beneficial result can be obtained despite lowlight levels typical for deep tissue imaging. Furthermore, algorithmsfor processing spectrally resolved image data are disclosed that areuseful not only for deep tissue imaging, but for processing spectrallyresolved image data of biological samples in general. In some cases, theprocessing can proceed in a highly automated fashion, without little orno user-guidance, while still producing accurate images of one or moreselected components in the biological sample.

Spectral imaging techniques (e.g., fluorescence imaging) can be usedwith samples that are labeled with fluorescent tags or are geneticallymodified so that one or more target compounds fluoresce or which aresimply targets that fluoresce. This fluorescence from one or more targetcompounds is called “target fluorescence.” However, in addition tocomponents such as the target compounds, a sample may include othercomponents that each emits radiation according to a correspondingemission spectrum. For example, there may be other compounds ormaterials in a sample that are not labeled or are not geneticallymodified that may also fluoresce at some level. This unknown and/orbackground fluorescence is called “autofluorescence.”

This autofluorescence interferes with quantitative measurements oftarget fluorescence, and in some cases can be many times brighter thanthe target fluorescence. Some particular applications of interest are inthe life sciences, including in-vivo fluorescence imaging, andfluorescence microscopy; however, the techniques described here are alsoeffective in other applications such as bright-field microscopy.Autofluorescence can also include other spectral signatures such asinstrument spectral responses, or absorptions from other constituents orchromaphores in transmitted light (e.g., bright-field) applications.Moreover, there may be multiple components, each of which give rise todifferent autofluoresence spectra.

Autofluorescence typically has emission spectra that differ from thetarget fluorescence emission spectra, providing an opportunity to applyspectral unmixing techniques to separate the target fluorescence fromautofluorescence in each detected signal spectrum at a pixel in adetected image cube. In other words, the signal spectrum can bedecomposed into separate contributions from the target fluorescence andthe autofluorescence. This spectral unmixing produces a high contrastimage of the labeled or genetically modified portion of the sample byremoving the portion of a detected signal that can be attributed toautofluorescence. In some cases, more than one compound is labeled ormodified with the goal being to produce multiple images representingconcentrations of each target compound isolated from theautofluorescence and the other target compounds.

To accurately apply spectral unmixing tools, it is useful to obtain thepure emission spectrum of each target compound and of eachautofluorescence component that may contribute to an overall detectedspectrum. The more accurately the spectral shapes of such componentspectra are known, the more accurately the mixed signal spectra in theimage cube can be unmixed or decomposed to generate individual imagesrepresenting quantities of target compounds and/or autofluorescencecomponents. The pure emission spectrum for a given component is thesignal spectrum that would be measured if only that componentcontributed to the measured light, such as, for example, if thatcomponent were isolated from the other components.

However, rarely are the target compound spectra represented in their“pure” form within a signal spectrum at a given pixel of the detectedimage cube. In some cases, published spectra of various fluorescentlabels can be used. For accurate results in these cases, the systemresponses should be calibrated out of the system being used, as well asthe system on which that the spectra were originally measured. Anotherway to obtain the spectra from each target compound is to individuallyisolate each target compound and collect the spectrum of each on thesame system to be used for subsequent in-vivo experiments. However, thisisolation of target compounds is often impractical. Also, the spectraresulting from labels often change when the labels are in in-vivosamples, making spectra collected from target compounds isolated fromthe in-vivo sample inaccurate.

In some cases it is useful to process a sample in its current statewithout a priori knowledge of the component spectra represented withinthe signal spectra of an image cube obtained from the sample. Thereforeit is useful to be able to accurately determine the individual componentspectra based on the image cube data so that spectral unmixing can beperformed and images of individual target compounds can be generated.Techniques described below, such as the “remainder techniques,” areparticularly well suited for biological samples due to the typicalcharacteristics of the component spectra involved, as described in moredetail below.

Another useful aspect of these techniques described below is theircompatibility with automation techniques. For example, in someimplementations, it is not necessary to have a user visually identifyfeatures in an image based on specialized knowledge. In otherimplementations an unskilled user can provide feedback based on aninitial automated step.

Imaging System

A schematic diagram of a spectral imaging system 100 for imagingbiological samples is shown in FIG. 1. System 100 includes a sampleholder 110 suitable for holding a specimen 112. For example, thespecimen 112 may be a living organism, such as an animal or mammal. Atarget compound is associated with (e.g., bound to or accumulated in)selected portions of tissue (e.g., deep tissue) in the specimen 112. Anilluminator 120 (e.g., a metal halide lamp or other lamp, a laser, anlight emitting diode array, or any other source of electromagneticradiation) directs excitation light 122 to the specimen 112 to exciteemission (e.g., fluorescence) from the target compound in the tissue.Typically, the excitation light will also cause the autofluoresence fromthe other components in the specimen 112. Therefore, the electromagneticradiation 130 emitted from the specimen 112 includes emission from thetarget compound as well as autofluorescence. Emitted radiation 130 iscollected by imaging system 140 and imaged onto a camera 150.

Because system 100 is designed to be able to image deep tissue inrelatively large specimens (e.g., living organisms), the imaging systemtypical provides a demagnification of one or more, or even 2 or more.That is, the image on the camera is the same size or smaller than theobject field of view for the imaging system. Also, the object field ofview for the imaging system is typically greater than about 2 cm (orgreater than about 3 cm) along a diagonal dimension.

Furthermore, although FIG. 1 shows emitted radiation 130 as beingcollected from an opposite side of the specimen 112 relative toexcitation light 122, in other embodiments, the emitted radiation can becollected from the same side as, or at an angle to, that illuminated bythe excitation light. Moreover, illumination may be provided frommultiple sides of the specimen 112.

Positioned between the specimen 112 and camera 150 is a tunable opticalfilter module 160 (e.g., a liquid crystal, tunable optical filter, aninterferometric optical filter, or a motorized filter wheel). Opticalfilter module 160 spectrally filters emitted radiation 130 according toeach of a plurality of spectral weighting functions (for example, fouror more spectral weighting functions). The spectral weighting functionsmay correspond to specific wavelength bands, or may be more complicatedfunctions such as a sinusoid distribution of pass bands. The opticalfilter module 160 may also optionally include other filters including,for example, a filter that reduces the amount of excitation light 122that can enter the camera 150. Camera 150 records images of the spectralfiltered emitted radiation 170 for each of the different spectralweighting functions, and sends the image data to a computer 180 foranalysis. As described in greater detail below, the computer processesthe image data based on the different spectral weighting functions, andone or more emission spectra corresponding to pure target compound, pureautofluorescence of one or more other components of the specimen 112, orboth, to construct an image that suppresses the autofluorescence signalto reveal the target compound.

In some implementations, a portion of the system 100 (e.g., the portionof the system 100 between the illuminator 120 and the camera 150,inclusive) is optionally enclosed in a housing 190, for example, toreduce the amount of stray light (e.g., room light) that can be imagedonto the camera 150 or that can interact with the specimen 112.

Spectral Imaging Techniques

In what follows, we describe the context for the spectral imaging of thebiological samples, specific examples including deep tissue imaging, andspectral unmixing techniques for constructing the images.

Signal Strength

It is a hallmark of imaging structures in biological samples (andparticularly in deep-tissue samples) via target compounds that theoptical signals are relatively weak. Accordingly, many practitionersplace prime importance on the properties of the detector, and on theefficiency of all elements in the optical path, such as the lenses andthe blocking filter used to block the excitation light from reaching thedetector. Yet while the present art of CCD detectors and the like issuitable for detecting low light level signals, it does not adequatelyaddress the problem of discriminating between light emitted by thetarget compound, and light from other sources such as autofluorescence.Thus one's detection level in practice may be set by the level ofconfounding light arising from sites elsewhere within the specimen,rather than considerations such as readout noise in one's detector, orthe light gathering power of the objective.

Put more precisely, one's detection limit can be seen as the greater ofone's detector noise or the confounding signal flux which is presentedto the detector; expressed in either case as the equivalentconcentration of target compound in the specimen to produce light ofthat signal level at the detector.

Unless the light emitted by the target compound dominates over all othersources in the specimen, one is often limited by the confounding signalrather than by one's detection apparatus. Some level of autofluorescenceis inherent in biological samples when they are illuminated with lightof visible range, especially when the light is green (550 nm) orshorter. So despite the use of optimized target compounds,autofluorescence arising at or near the specimen surface can often setthe detection limit.

Further, emission from target compounds within deep tissue can beattenuated by scattering or absorption as it travels from the site ofemission to the surface of the specimen. The signal level reaching theimaging system is thus reduced, while light that is generated at thesurface layer of the specimen is not similarly attenuated. The detailsof this effect depend on the geometry of the sample specimen relative tothe collection optics, as well as the optical properties of the sample.

Likewise, the illumination light may be attenuated or scattered as ittravels from the source of illumination through the surface layers ofthe specimen on its way to the structure being imaged. The excitationsignal reaching the target site is reduced, while the signal developedat the surface of the specimen is not similarly attenuated. The detailsof this depend on the geometry of the illumination and collectionoptics, as well as on the optical properties of the sample specimen.

These considerations can exacerbate the effect of autofluorescence byincreasing the relative contribution of autofluorescence emission to thedetected signal, compared with emission from the target compound.

The magnitude of the problem is illustrated by FIG. 2, which is an imageof the fluorescent emission from a mouse. The mouse was illuminated withlight of approximately 480 nm, and the emission light was filtered by a25 nm bandpass filter centered at 530 nm. There is a tumor in the lungof the mouse which expresses the green fluorescent protein (GFP). Yetthe tumor is not easily distinguishable in the image due to an equal orgreater signal from generalized autofluorescence, apparently developedin the dermal layers of the mouse. As a result, while the signal levelat any point in the image is easily quantified, the presence of targetcompound, and thus the tumor, cannot be confirmed due to high levels ofbackground autofluorescence which sets an equal or higher detectionthreshold than the target compound involved.

Autofluorescence is also variable from specimen to specimen and can beunpredictable. Thus if an absolute flux level is used to makeassessments about the target compound, one can obtain false positivereadings. Variability can arise from factors such as mold or disease onthe skin of the specimen. These are typically not uniform across thespecimen. So if one seeks to detect the presence of a target compound bycomparing local signal levels in a given region against the mean levelfor the specimen, results are also not reliable.

Spectral Crosstalk

It is possible in some cases to reduce autofluorescence by choice of theillumination wavelength. Generally the use of longer wavelengths forillumination is beneficial, as is known in the art, since they typicallygenerate less autofluorescence. Also, it can be beneficial to choose atarget compound whose emission light occurs at a different wavelengthrange from the autofluorescence of the specimen. Yet it is typically notpossible to choose an illumination wavelength such there is nocrosstalk. In the example shown in FIG. 3, the emission spectra of thetarget compound, GFP (shown by the dark-shaded line), and of theautofluorescence (shown by the light-shaded line), are overlapping. Atany wavelength where the target has substantial emission, theautofluorescence is also strong, so autofluorescence cannot beeliminated by use of a fixed optical filter or something similar. Nordoes a color camera discriminate between two such similar green spectra.

Yet as FIG. 3 indicates, the spectra of GFP and autofluorescenceemissions are nonetheless different. Thus, a spectral imaging approachcan distinguish the two and eliminate or greatly reduce the contributionof the latter signal.

Spectral Unmixing

One tool for separating each pixel's signal spectrum into its componentspectra is linear decomposition or unmixing. For example, one canperform a least-squares best fit approximation to determine how much ofeach individual component spectrum would be required to most accuratelyrecreate the measured signal spectrum.

Spectral unmixing uses a set of input spectra to represent the componentspectra within the detected image cube. Typically, the input spectra areestimates of the pure spectra for the different components. One approachfor providing input spectra for unmixing, if the component spectra arenot available from a library, or have not already been collected fromisolated component materials, is to assume that each of the individualcomponent spectra can be represented by the spectrum of a pixel orgroups of pixels within the image cube.

However, this approach typically produces a poor estimate for at leastone or more of the pure spectra. For example, rarely do the targetfluorescence components exist by themselves, separate from theautofluorescence components, in isolated areas within an image cube. Thesignal spectrum for a region of pixels in which a target compoundappears is usually a mixture of component spectra. Therefore, if oneestimates the pure spectrum for one of the target components based onsuch a region, the unmixing will be poor—it will represent mixtures ofcompounds associated with the mixed component spectra.

Also, in some cases it is difficult to know a priori how many actualfluorescing components (each associated with a corresponding componentspectrum) exist within a sample, in order to accurately apportion themeasured signal within the image cube. If some component spectra are notrepresented during unmixing then the relative ratios of the remainingcomponents will not be accurately determined.

In some cases, manually choosing from where to select the input spectraand choosing how many input spectra to unmix can work if the user canmake an educated guess based on specialized knowledge and/or experience.Some users can work effectively this way if they have significant apriori knowledge of the sample.

A good example of the pitfalls of these approaches is shown in FIGS. 4Aand 4B. In the image of FIG. 2, a skilled user can faintly discern anarea (of bluish hue in a corresponding color image) about where the lungshould be. To an untrained eye, this irregularity caused by the tumorwould be imperceptible. If one uses the signal spectra from surroundingareas as input spectra to represent autofluorescence, and the signalspectra from the area which looks like the tumor as input spectra torepresent target fluorescence, and performs an unmixing using theseinput spectra, one arrives at the images shown in FIGS. 4A and 4B. FIG.4A is the image of the portion of the field of view that most closelyspectrally resembles the pixels corresponding to the area that wasidentified as autofluorescence. FIG. 4B is the image of the portion ofthe field of view that most closely spectrally resembles the pixelscorresponding to the area that was identified as the tumor. The areaassociated with the tumor is well defined. However, there are problemswith the images. For example, the “hole” left in the autofluorescenceimage (FIG. 4A), indicates that photons that were emitted from this areawere inaccurately apportioned to the labeled tumor image.

If one wants to measure the amount of target fluorescence as a estimateof tumor size, this crosstalk can lead to significant error (e.g., ameasured magnitude larger than the actual target fluorescence). Such anerror occurs in this example because, even though the input spectrumused to represent the autofluorescence is primarily autofluorescence,the input spectrum representing the tumor is a mixture of targetfluorescence and autofluorescence since the target compound andautofluorescing material are co-localized. To perform the unmixingcorrectly in this example, not only should the input autofluorescencespectrum be “pure,” but the and input target fluorescence spectrumshould also be “pure” (e.g., not being mixed with significant amounts ofother component spectra, in this case autofluorescence).

Unmixing with Pure Spectra

Even though in some samples a component spectrum may not be representedin its “pure” form (i.e., substantially unmixed form, e.g., mixed withless than about 10% other components, or preferably less than about 1%other components) in any pixel's signal spectrum, an estimate for thepure component spectrum can in some cases still be obtained from theimage cube data to use as an input spectrum for unmixing. For example,in some cases one component spectrum (e.g., corresponding toautofluorescence), identified as spectrum A, may be available in a“pure” form (e.g., from a pixel's signal spectrum or otherwise known oravailable). Another component spectrum, identified as spectrum B,however, may not be available in a “pure” form. In such cases, ifspectrum B is represented in the image cube mixed only with spectrum Aat one or more pixels that can be identified, then spectrum A can besubtracted from this mixed “A+B” spectrum to obtain a pure spectrum fromB. The technique for more accurately estimating the pure spectra fromthe image cube is described further below. First, however, we describean example in which spectral unmixing or decomposition of image cubedata into contributions from estimates of pure spectra for differentcomponents of the sample was successfully used to image a targetcomponent in a deep tissue sample.

In this example, the specimen was illuminated and the illumination lightfrom was blocked form entering the detector. This can be done using anilluminator such as the LT-9500 MSYS from Lighttools Research(Encinitas, Calif.) together with a longpass optical filter thattransmits substantially all light λ>510 nm, placed in the path of theobjective.

The spectral imaging detector included a QImaging 1300C digital cooledCCD camera (Roper Scientific, Trenton N.J.) with a 55 mm F/2.8 Nikkormacro lens (Nikon USA, Melville N.Y.), to which a VARISPEC tunableoptical filter (CRI Inc, Woburn Mass.) was coupled with a mountingadaptor. The VARISPEC filter is a computer-controlled optical filterwith 25 nm bandpass and tunable center wavelength. These were connectedto an IBM Thinkpad computer which controls the image acquisition andperforms the data analysis. Communication is via an IEEE-1394 interfaceto the camera, and an RS-232 interface to the VARISPEC filter.

The VARISPEC filter uses nematic liquid crystal variable retarderelements to construct a tunable Lyot filter. The variable retarders areplaced in optical series with fixed waveplates of quartz or othermaterial, to produce a retardance that is well-known and electricallyadjustable. Linear polarizers between successive stages of the filteract to block unwanted orders so only a single peak is transmitted, andout-of-band leakage can be reduced to 0.01% if desired. By choice of theretarder thicknesses, one may obtain bandwidths ranging from 0.1 nm to50 nm or more. Tuning action is rapid (<50 ms) and there is no imageshift from tuning, which is valuable for imaging applications.

Referring to FIG. 5, the mouse of FIG. 2 was imaged by taking a sequenceof images S(x, y, λ_(i)) (for i=1 to n, the number of spectral settings)each recorded using a spectral weighting function I_(i) determined bythe VARISPEC filter, while the center wavelength of the VARISPEC filterwas tuned from 500 nm to 650 nm. This sequence of images in FIG. 5 is anexample of a spectral image cube 500. The different spectral spectralweighting functions (in this case, spectral passbands) are shown in FIG.9. The result is an image cube 500, with a full two-dimensional image ofthe sample for a given center wavelength λ_(i), and a full spectrum at agiven pixel (x,y) in the images. The exact spectrum recorded at a givenpixel depends on the amount of GFP and autofluorescence, and on the twospectra, as:

S(x,y,λ)=a(x,y)*F(λ)+b(x,y)*G(λ)  [1]

where (x, y) indices are used to denote a given pixel location in theimages, the asterick “*” denotes multiplication, λ is used to denote agiven wavelength (or wavelength band) of emission or detection, and

S(x, y, λ) denotes the net signal for a given pixel location andwavelength,

F(λ) denotes the emission spectrum of autofluorescence,

G(λ) denotes the emission spectrum of GFP,

a(x, y) indicates the abundance of autofluorescence at a given (x, y)pixel location, and

b(x, y) indicates the abundance of GFP at a given (x, y) pixel location.

Equation [1] states that the net signal from a given pixel location isthe sum of two contributions, weighted by the relative amount ofautofluorescence and GFP present. It is easier to see if one writes theabove equation for a single pixel:

S(λ)=aF(λ)+bG(λ)  [2]

F and G may be termed the spectral eigenstates for the system becausethey correspond to the pure spectra for the autofluorescence and GFPemission, which are combined in various amounts according to the amountof autofluorescence and GFP emission, to produce an observed spectrum orsignal spectrum S. Thus, the signal spectrum is a weighted superpositioncorresponding to separate contributions from the autofluorescence andthe GFP emission.

Now if the emission spectra of the autofluorescence and of the GFP areknown (or can be deduced, as described below), one may invert equation[2] by linear algebra to solve for a and b, provided that the spectrum Shas at least two elements in it; i.e. that one has data for at least twoemission wavelengths λ. Equation [2] can be re-written as S=E A. Then wecan write

A=E ⁻¹ S  [3]

where

A is a column vector with components a and b, and

E is the matrix whose columns are the spectral eigenstates, namely [FG].

Using equation [3] one can take the observed signal spectrum andcalculate the abundance of the autofluorescence and of the GFP sources(e.g., the components that produce autofluorescence and GFP emission) atthe corresponding pixel location. This process may be repeated for eachpixel in the image, to produce an image of GFP that is free ofcontributions from autofluorescence. As a result, the detection level isgreatly enhanced.

Note that the matrix E need only be inverted once for a given set ofautofluorescence and target compound spectra, so the calculation ofabundances is not burdensome and can be readily done in nearly real-timeby a personal computer.

The results of this spectral unmixing process are shown in FIGS. 6 and7, which present the abundance images for GFP and autofluorescence,respectively. As FIG. 6 shows, it is easy to detect the GFP once it isseparated from the autofluorescence. The degree of improvement in theGFP image is striking. Also, one can see that the autofluorescence image(FIG. 7) is smooth and unaffected in the region where GFP is present,which is consistent with the fact that the presence of GFP in a deeptissue structure should not alter the amount of autofluorescenceemission from the overlying dermal regions.

Overall, an exemplary measurement and analysis process is shown as ablock diagram in FIG. 8. The specimen is prepared and illuminated (step805) and the spectral bands to be acquired determined (step 810). Thenthe spectral filter is set to transmit the spectral weighting functionI_(i), for example, a particular wavelength band (step 815), and animage corresponding to that spectral weighting function is acquired(step 820). The spectral filter is then set to the next spectralweighting function and the corresponding image acquired until all bandshave been acquired (steps 825 and 830). The spectra for the targetcompound and the autofluorescence are then provided or otherwisedetermined (step 835). Based on the spectra, the matrix E is generatedand its inverse determined (step 840). For each image pixel, the signalspectrum defined by the series of acquired images is then multiplied bythe inverse matrix of E (step 845) to generate an abundance image of thetarget compound(s), i.e., the sample image (step 850).

In this example, spectral imaging permitted observation of structures intissue lying ˜2 mm within a living organism, where the overlying dermisis at least 300 microns thick and has significant autofluorescence.Spectral imaging has also been used to image structures at differingdepths in other specimens, including non-mammalian specimens such aszebrafish. In the latter, the specimen is physically thinner, but onceagain there is the problem of autofluorescence arising from other layersin the specimen, which confounds the detection of target compounds inthe interior of the specimen. While there are optical techniques fordepth sectioning, such as confocal microscopy, the spectral imagingtechniques described herein provide a simple and practical alternative.

An embodiment operating in the infrared range 600-1100 nm may also beconstructed using a near-infrared VARISPEC filter such as the modelVIS-NIR2-10-20HC.

Nothing about these techniques prevents one from viewing multiple targetcompounds per specimen (e.g., m target compounds). If we denote thenumber of spectral settings as n, matrix E becomes an n×m matrix insteadof an n×2 matrix used in the above example. So, one can use thesetechniques to remove autofluorescence from a sample which contains twotarget compounds; or to remove two types of autofluorescence from asample with one or more target compounds. In any case, the result is theisolation of the target compound(s) from the autofluorescence, and theability to quantify one or all of these components.

The limit to the number of compounds that can be isolated, and to thesignal to noise ratio generally, is given by the shot noise levels andthe degree of spectral distinction between the emission spectra of thespecies being distinguished (including autofluorescence). One candescribe the degree of correlation between two spectra by a spectralangle distance θ, defined by

θ=arccos [(S ₁ ·S ₂)/(|S ₁ ∥S ₂|)]  [4]

Sets of spectra for which θ is small for two members are not as easilyseparated into their components. Physically, the reason for this iseasily understood: if two spectra are only marginally different, it isharder to determine which species was present, and noise can easilychange one's estimate of relative abundances. Criteria such as θ can beused to help decide what spectral bands are appropriate for ameasurement, and one may try and select bands that yield a large θwhenever possible. Or, one may make an empirical study of what bandsyield the best separation, by trial and error. It can be helpful toinclude more bands than would appear necessary from mathematicalanalysis alone, in order to reduce sensitivity to slight spectral shiftsfrom the expected shapes, as may occur due to variation betweenspecimens and the like.

Generally, the signal spectrum measured for each spatial location (i.e.,pixel) in the sample and the estimates of the pure spectra used for theunmixing should typically include enough values to provide accurateinformation about the components of interest. For example, for atwo-component sample analysis, it is preferable that there be at leastthree values (or more preferably, four or more values) corresponding todifferent spectral weighting functions (e.g., different spectral bands).As the number of components increase, the number of values for thesignal and pure spectra should also increase.

It is worth considering the optical efficiency of the measurementapparatus in the above embodiment, to understand where the potentialimprovement provided by these techniques comes from. First, the lensused was an F/2.8 type instead of an F/1.2 or F/1.8 which is moretypical for this work, and this choice results in 2.4-5.4× less lightcollection. Next, the VARISPEC filter has a transmission ofapproximately 25 percent, and collects over a 25 nm range, in contrastto a typical interference filter which has a transmission of 80 percentand collects over a 40 nm range. This further reduces the sensitivity bya factor of 5.1× compared to equipment that might be used for this work,for an overall reduction in light flux of 12.3×-27.8× compared to somealternatives of the art.

The CCD camera is cooled 25° below ambient to approximately 0° C., whichis typical for an ordinary CCD sensor, unlike the sensors used inimaging stations such as the ChemiPro system from Roper Scientific(Trenton, N.J.)., which is cooled with liquid nitrogen to attaintemperatures 100° below ambient or lower.

As this suggests, the effectiveness of these techniques does not arisefrom extreme efficiency in the gathering or collection of light; ratherit comes from using spectral selectivity to identify and remove theeffects of background autofluorescence.

In other embodiments, the spectral weighting functions may be differentfrom passbands. What is important is that the spectral weightings of thevarious images be different. For example, one could use aninterferometer to acquire the spectral information, as shown in FIG. 10in block-diagram form. The spectral response of the interferometer isshown in FIG. 11 for some selected values of path difference Z. Imagesthus obtained can be used for practicing the invention, either directlyor after transforming from interferograms to spectra. The suitability ofusing the interferograms can be checked by looking at how well theydistinguish between the species involved, which can be determined bymeasures such as cos θ or by experimental study.

The block diagram of FIG. 10 is similar to that of FIG. 8 except that:steps 815, 820, 825, and 830 are replaced with corresponding steps 1015,1020, 1025, and 1030, which use a interferogram as the spectralweighting function rather than particular spectral bands; there is anoptional step 1032, which describe Fourier transforming the spectrallyfiltered images to generate a spectral cube; and step 835 is replacedwith step 1035 determines spectra or interferogram weightings for thetarget compound and the autofluorescence consistent with the form of theacquired data (and optional Fourier transform) for use in generating thematrix E.

The interferometer can be a mechanical type such as a Sagnac design, orit can be a birefringent interferometer as described in U.S. Pat. No.6,421,131, “Birefringent interferometer”. The latter uses fixed retarderelements such as quartz plates, together with switching apparatus, tomake the retarders add or cancel one another, so that using theseelements, along with variable retarder elements, one can produce anydesired retardance within a wide range. When polarized light encountersthis assembly, its polarization state is changed in a manner thatdepends on the wavelength of light, and this can be detected at an exitanalyzer polarizer. The spectral response at any particular setting ofthe interferometer is a sinusoid in 1/λ, after allowing for dispersion.By taking a sequence of readings at known retardance values, andperforming a fourier transform, the spectrum of the light can bedetermined. Such apparatus can be used in imaging systems to obtain aspectrum at every point in an image, or simply to obtain a set of imageswith various sinusoidal spectral response functions in the members ofthe set.

More generally, any spectral imaging apparatus can be used provided thatit yields adequate spectral information to distinguish emission by thetarget compound from background autofluorescence.

Accurately Estimating Pure Spectra

We now turn to the question of how the pure spectra F and G in theexample above were determined, and more generally, how to accuratelyestimate pure spectra in the first place for use in the decomposition(i.e., unmixing) of the signal spectrum into their component spectra.FIG. 12 shows the steps in block-diagram form of a spectral unmixingtechnique in which one estimates the pure spectra for use as inputspectra in the spectral unmixing. This step is sometimes referred to asspectral component decomposition to obtain pure input spectra. This isnot to be confused with the decomposition (or unmixing) of the signalspectrum at each pixel into relative contributions of the pure inputspectra in the unmixing. The block diagram is similar to that of FIG. 8,except that step 835 is replaced with performing a spectral componentdecomposition (step 1235) and determining eigenvectors corresponding tothe autofluorescence and target compound(s) (step 1237) for use in thematrix E in step 840.

In general, any method may be used which yields an adequate estimate ofthe spectra involved. For some target compounds, there is a knownspectrum for the material from published references. Alternatively, witha spectral imaging station as described herein, one may obtain thespectrum directly by placing a sample containing a sufficientconcentration of the target compound in front of the imager and takingits spectrum. Conversely, it is often possible to image a region of thespecimen where one has a priori knowledge that there is no targetcompound in that region, and in this way one can obtain an estimate ofthat component. Various data analysis techniques can be used indetermining component spectra for spectral unmixing, such as principalcomponent analysis (PCA), which identifies the most orthogonal spectraleigenvectors from an image cube, and yields score images showing theweighting of each eigenvector throughout the image. If PCA analysis isperformed on an image that contains contributions from the targetcompound(s) and from the background autofluorescence, the vectors fromPCA can be used to develop estimates of the spectra involved.

This may be done in combination with other mathematical processing, andthere are other known techniques for identifying low-dimensionalityspectral vectors, such as projection pursuit, a technique described inL. Jimenez and D. Landgrebe, “Hyperspectral Data Analysis and FeatureReduction Via Projection Pursuit”, IEEE Transactions on Geoscience andRemote Sensing. Vol. 37, No. 6, pp. 2653-2667, November 1999. Othertechniques include independent component analysis (ICA), projectionpursuit, and end-member detection algorithms, for example.

These techniques are typically not well-suited to the applications inthe life sciences. For example, some techniques are optimized forspectral imaging data sets that contain spectra with dense spectralshapes and well-defined narrow peaks. In some techniques the spectralranges are large compared to the individual spectral features and peaksthat are used for analysis. The presence of peaks, or the ratio of peaksmay be then used to classify “end-members” to be separated.Unfortunately, the components in biological sample typically do not havesuch well-defined, narrow peaks.

Another issue with some techniques is that they output images related tospectra that are present in a pure form somewhere within the originalimage cube. In many cases in the life sciences, signal spectra presentin the image cube are mixtures of components. In the case of the labeledtumor in a mouse, it is unlikely to ever find a location on the mousewhere tumor exists and the autofluorescence does not. If the componentof interest is not in a pure form somewhere in the original image cube,then it is unlikely that these techniques will output an image thataccurately represents the abundance of the component of interest.

There are some techniques, sometimes call ‘convex-hull’ algorithms, thatestimate what the true end-members are even if they do not exist in apure form in the image, but the effectiveness is dependent on how closesignal spectra in the image cube are to the end-members.

Another issue with some of the techniques is that they operate only inan unsupervised way which gives less opportunity to “steer” them in theright direction with information that is available from knowledge of thesample.

When most of these techniques are used with life sciences samples, theoutput images representing abundances of components often do notcorrelate well with known physiological or anatomical features in thesamples. Some techniques work well in some cases, while others work wellin other cases.

Shown in FIGS. 13A, 13B and 13C are exemplary component abundance imagesgenerated by a typical non-life sciences (e.g., remote sensing)end-member detection algorithm based on the image cube for the mouse ofFIG. 2. Note that none of the output images appear to correspond withjust the tumor or just the autofluorescence in the skin.

In the life sciences, and in particular fluorescence applications, thespectral features are broad and smooth, often spanning most of thedetected spectral range. Usually the component spectrum (or spectra) ofinterest, the target fluorescence, shows up as a modification to theoften larger and overwhelming autofluorescence signal. Key informationused to analyze samples and determine composition lies in the individualdetection and quantification of highly overlapping spectral components.Obtaining such information is better suited to techniques that are ableto operate on subtle shifts in spectral shape. Techniques that are moreappropriate for life sciences applications should be good at firstrejecting the largest component, the autofluorescence, and thenaccurately separating away and quantifying smaller, overlapping spectralcomponents. These characteristics are distinctly different from those ofnon-life sciences (e.g., remote sensing) applications.

We have discovered that one approach that does work well for accuratelyestimating pure spectra for deep tissue imaging is to use one estimatefor the pure spectrum of one components that is easier to obtain and useit to help reveal the pure spectrum of another component from data inthe image cube where both components are present. Implementations ofthis include techniques that calculate a remainder spectrum, which aredescribed in greater detail further below.

Remainder Techniques

Some techniques for reducing the autofluorescence without having apriori knowledge of the target compound spectrum involve looking at thesignal spectrum S(λ) for a given pixel, and subtracting from it themaximum amount of autofluorescence F(λ) while leaving the remainingsignal that is positive definite in all spectral channels. That is, onedefines a so-called remainder spectrum R_(a)(λ) for each pixel:

R _(a)(λ)=S(λ)−aF(λ)  [5a]

and then selects the largest value of parameter a consistent withR_(a)(λ) having a non-negative value in every spectral channel. Theresulting spectrum R_(a)(λ) is then used as the signal spectrum,expunged of autofluorescence. One may also make the determination ofparameter “a” based not on strict non-negative criterion listed above,but on some related criteria that incorporates a small negativedistribution, to account for considerations such as shot noise ordetector noise. Additional examples of optimization criteria forremoving the maximal amount of autofluorescence spectrum include usingdifferent error functions, some of which are described in more detailfurther below.

Alternatively, one may seek to determine the distribution of the targetcompound by a similar method when its emission spectrum is known, butthe autofluorescence spectrum is not, by seeking to subtract off fromS(λ) the maximum amount of target emission G(λ) consistent with apositive remainder, and then reporting the amount that was subtracted ateach pixel as the image of the target compound. In this case, theremainder spectrum R_(b)(λ) for each pixel is given by:

R _(b)(λ)=S(λ)−bG(λ)  [5b]

where one selects the largest value of parameter b consistent withR_(b)(λ) having a non-negative value in every spectral channel.

Furthermore, the remainder technique described above in connection withEquations 5a and 5b can be expanded to cases where the spectra for oneor more additional components of the sample are known, and one wants toremove their contribution to the signal. In such cases the remainderspectrum is rewritten to subtract a contribution of each such componentfrom the observed signal based on the additional spectra and consistentwith a positive remainder in each spectral channel.

The remainder technique assumes that an initial estimate for at leastone of the component spectrum is provided. It can be provided by a priorknowledge or measurement, or it can be determined based on the imagecube data itself. In the latter case, it can be determined byuser-guidance (i.e., a supervised technique) or without user guidance(i.e., an unsupervised technique). For example, a user may be able toidentify at least one region of the spectral image cube in which onecomponent is nominally isolated, and thereby determined the purespectrum for that component from the data in that region (e.g., byaveraging the signal spectrum from the pixels in that region). Inanother example, without user-guidance, it might be assumed that becauseautofluoresence will dominant the spectral information in the imagecube, one can simply average the spectral information in every pixel todetermine an estimate for the pure spectrum of the autofluorescence.

Furthermore, the remainder spectrum can be calculated at one, some, orall of the pixels. For example, in the two-component case describedabove, the deep tissue image could be constructed directly from theremainder spectrum in each pixel, which is now assumed to be associatedwith only the target compound. In other examples, however, the remainderspectrum may be calculated for only one, or some pixels, as part of amore involved process for estimating pure spectra. Furthermore, theremainder technique may be applied to a preprocessed data set derivedfrom the image cube. For example, the signal spectrum from some or allof the pixels in the image spectrum may be averaged to produce processedsignal spectrum to which the remainder technique then is applied.

The more detailed technique below, which also involves calculatingremainder spectra, is illustrative of these various possibilities. Thetechniques works well even with image cubes representative of the moredifficult samples that one would encounter in fluorescence in-vivoimaging, fluorescence microscopy, and bright-field microscopy.

More Detailed Example of a Remainder Technique

Step 1: Determine Number of Components

A first step in some techniques for obtaining pure spectra isdetermining how many components are present in a given sample. Someremainder techniques include an automated step to delineate or identifyareas of a sample that represent or indicate the location of what areprobably individual components.

The remainder techniques are able to operate unsupervised, or with auser-provided spectrum as a starting point. In the unsupervised mode, afirst iteration uses the average of all the signal spectra in the imagecube as a first “spectral vector” to be subtracted. A best-fitapproximation is performed at each pixel to determine a quantity of thepixel's signal spectrum that can be apportioned to the spectral vector.This quantity of the spectral vector is then subtracted out of the imagecube data and a component image is generated representing the quantitythat was subtracted. In subsequent iterations, it is assumed that theareas of highest intensity (after subtraction) are the next mostspectrally pure component and those areas are used to obtain the nextspectral vector. (The “intensity” at a pixel location is calculated byintegrating the signal spectrum at that pixel over all wavelengths.) Thenumber of iterations of subtracting spectral vectors that are performedcan be specified up front, or can be continued until the amount ofremainder signal left over is reduced below a specified percentage.

This approach has the advantage that often the spectral differences,that are “pealed” away and put into separate component images, are oftenvery close to the pure spectra from the components. In cases where it isnot, some user input is possible to guide it. For example, if the firstspectral vector is the average spectrum of the whole image cube and isnot close to one of the component spectra, which may happen for examplewith a shaved mouse instead of a nude mouse, then the user can point inthe image at what should be autofluorescence, and the process can usethat as the first spectral vector.

FIGS. 14A, 14B and 14C show output images generated by an unsupervisediterative remainder technique described above on the image cube for themouse of FIG. 2. Note that these first three components correspond tofeatures that make sense anatomically—an autofluorescence image from theskin (FIG. 14A), an autofluorescence image from the sebum around hairfollicles (FIG. 14B), and the labeled tumor image (FIG. 14C) withrelatively low signal from other components.

This spectral component decomposition technique is useful for providingcomponent images that indicate to the user where the most significantcomponents are and indicate from where in the image spectra should beextracted as input into the next iteration. Sometimes the decomposedcomponent spectra are close enough to the actual spectra to be goodestimates for the pure component spectra. If this is the case, thecomponent images generated by the iterative remainder technique aresufficiently pure for whatever quantitative analysis is planned, such astumor measurement or co-localization determination. However, sometimes,the decomposed component spectra are not accurate enough because theinitial spectral vector uses the average spectrum from the entire image,and the component images represent some mixtures of components.

Step 2: Calculate Pure Component Spectra

After determining how many components are present in a given sample,pure component spectra can be calculated using the spectra extractedfrom the image cube data using the component images from the iterativeremainder technique in step 1.

One way to do this, and to further improve the accurate apportioning ofsignal into the appropriate component images, is to use the componentimages from step 1 as a guide for further analysis. In other words, step1 provides initial estimates for the pure spectra and component imagesbased on those initial estimates, which can then be used to improve theinitial estimates of the pure spectra.

For example, the user can choose component images from the iterativeremainder technique in step 1, based on knowledge of the sample, thatare thought to represent the a desired component (e.g., a component thatincludes a particular target compound) and an autofluorescence componentthat is co-localized with the desired component. The user first uses thecomponent images to choose a region (e.g., a set of one or morecontiguous or non-contiguous pixels) of the image cube corresponding tothe autofluorescence component without the desired component, which isassigned to spectrum “A.” The user then uses the component images tochoose a region of one or more pixels that show strong intensity of thedesired component (e.g., the tumor) along with the co-localizedautofluorescence, which is assigned to spectrum “A+B.”

These regions are then used to indicate pixel locations in the imagecube data from which to draw the spectrum “A”S_(A)(λ) and the spectrum“A+B”S_(A+B)(λ) used by the remainder technique. This process can berepeated for multiple desired components. The remainder techniqueapproximates a quantity Q of S_(A)(λ) is present in S_(A+B)(λ) using anoptimized error function. This quantity of S_(A)(λ) is then subtractedfrom S_(A+B)(λ) to leave the difference spectrum Δ(λ) (with an optionalconstant offset C) whose optimized value is used to estimate the purespectrum “B”S_(B)(λ). The difference spectrum is given by:

Δ(λ)=S _(A+B)(λ)−QS _(A)(λ)+C  [6]

The remainder technique determines optimized values for Q=Q^(opt), andoptionally C=C^(opt), by minimizing an error function. Any of a varietyof possible error functions may be used. One exemplary error functionerr₁(Δ) (where Δ≡Δ(λ) is implicitly a function of λ) is given by:

err ₁(Δ)=(e ^(−Δ)+1)Δ²  [7]

The remainder technique minimizes the average value of err₁(Δ) over λ.This error function err₁(Δ) is chosen to favor values of Δ that arepositive, since a negative spectrum S_(B)(λ) is not physicallyconsistent. Alternatively, a minimum mean squared error (MMSE) criterion(e.g., err₂(Δ)=Δ²) can be used along with a constraint that Δ bepositive. However, such a strict constraint corresponds to adiscontinuous error function that may not be as compatible with certaintypes of minimization techniques.

Other corrections can also be made. For example, a normalized errorfunction:

err ₃(Δ)=err ₁(Δ)/[S _(A+B)(λ)+S _(A)(λ)]  [8]

can be used to correct for larger values of err₁(Δ) being caused bylarger values of S_(A)(λ) and S_(A+B)(λ) that have the same relativemagnitudes. A Value of err₁(Δ) (or of err₃(Δ)) whose magnitude is belowa noise threshold value can be set to zero.

Once the optimized values Q^(opt) and C^(opt) have been calculated, thepure spectrum S_(B)(λ) is calculated as follows:

S _(B)(λ)=S _(A+B)(λ)−Q ^(opt) S _(A)(λ)+C ^(opt)  [9]

Step 3: Unmix Using Pure Component Spectra

After the pure component spectra are determined in step 2, unmixing canbe performed on the image cube data using these pure component spectra.This is sometimes effective in simple systems if the components areknown. However, it may not be optimal in some cases because it assumesall measured signal belong to one or another of the component images,thus forcing the sum of the signals in the unmixed images to equal thetotal detected signal. If the spectrum in the image cube at a particularpixel is made up of more components than is represented by thedetermined pure component spectra, then the unexpected component couldand probably will be inaccurately apportioned into a target compoundimage.

To solve this problem, the iterative remainder technique of step 1 canbe used in a “supervised” fashion where the spectra from Step 2 are usedas input into each iteration to “strip out” component images. Eachiteration produces a component image that corresponds to the spectrumthat was used as an input for that iteration. This has the advantagethat it does not force a sum of 100%, and leaves what is left over in aresidual image for further processing if desirable.

An example of how well this approach works is shown in FIGS. 15A and15B. FIG. 15A shows the autofluorescence with uniform intensity acrossthe area of the tumor, suggesting that the signals were accuratelyapportioned in the correct images. FIG. 15B shows the labeled tumor,appearing to be well isolated from other fluorescent components.

This capability can be configured in a variety of ways to provide veryeffective tools for examining and analyzing a sample. For example, itcould be configured to remove components from a data set as they arepointed to by a user with an input device (e.g., a mouse). Spectralcomponents both at the user selected locations and at other locations inthe image cube with the same spectral signature can be removed. Thespectrum for the selected component can be determined using Step 2, andremoved as described in Step 3. Also, the residual image that containssignal that did not make it into a component image can be examined tosee if there were any components that were not anticipated or knownabout.

Any of the steps by themselves and in any combination are useful. Steps1 and 2 have value by themselves in many cases.

FIG. 16 is a flow-chart diagram of a process 1600 that is illustrativeof the iterative remainder technique in step 1 above process. Process1600 extracts spectral vectors that can be used to calculate purecomponent spectra from image cube data. The resulting pure componentspectra can be used as input spectra for unmixing and/or stored forlater use in an spectral data library. A user provides 1602 image cubedata either by acquiring the data from a sample or by loading previouslyacquired image cube data.

A first spectral vector is then provided by any of a variety of methods.A user may select 1604 a first spectral vector from a library of spectra(e.g., a library of spectra representing various types ofautofluorescence for the types of samples being imaged). A user mayselect 1606 a first spectral vector from a region of interest (ROI)based on an image that represents autofluorescence (e.g., one of theimages in the image cube). A user may select 1608 a first spectralvector from a previously acquired image cube. Alternatively, the process1600 automatically determines 1610 a first spectral vector from theimage cube data (e.g., by the averaging the signal spectra from all ofthe pixels to calculate a “mean spectrum”).

The process 1600 then performs a non-negatively constrained unmixing onthe image cube to determine 1612 a quantity for each pixel representingthe amount of the current spectral vector that is to be subtracted fromthat pixel's signal spectrum. At each pixel, the process 1600 subtracts1614 the largest amount of the current spectral vector from the signalspectrum that can be subtracted without making the resulting remainderspectrum become negative at any wavelength. The process outputs 1616 acomponent image representing the quantity of the current spectral vectorthat was subtracted at each pixel and the corresponding current spectralvector that can be used in subsequent processing (e.g., informationprovided in a component image can be used to locate preferred spectrafor input into the PCSC process).

The process 1600 then determines the next spectral vector from a set ofone or more pixels that represent the largest “error” in the resulting“remainder image cube” (i.e., an image cube having the remainder spectracalculated above as signal spectra). For example, the process 1600selects 1618 the next spectral vector as the average of the spectra ofthe most intense N pixels in the remainder image cube (e.g., N=25).Alternatively, the process 1600 selects the most intense N pixels andthen chooses a subset R of the N pixels that are within a specifiedspectral angle distance θ of the most intense of the N pixels.

The process 1600 repeats these steps 1612, 1614, 1616 and 1618 until aminimum intensity level has been reached or a specified number ofspectral vectors have been found 1620.

Additional Embodiment

The techniques above are applicable to a wide range of deep tissuesamples, including, for example, living organisms, mammals (e.g.,humans, rodents, etc.), subdermal tissues, organs, etc. For example, thesample can also be a zebrafish in an aqueous sample stage. Moregenerally, the techniques above, for example the techniques forestimating pure spectra, can be used for non-deep-tissue biologicalsamples, including, e.g., tissue slices, cells, microscope slidesamples.

The sample holder will depends on sample but, in addition to includingsample holders for holding an animal such as a mouse, they can include,for example, a culture dish, a microtitre plate, a protein array, a DNAarray, a gel plate, or a tissue micro array plate. For in vivo imaging,the sample may be a surface on which a subject or animal sits, rests, oris immobilized.

Of course, target compounds different from GFP may be used. For example,the same apparatus may be used to view a mouse, or any other sample,that has been transfected to express either the yellow fluorescentprotein (YFP) or the red fluorescent protein (RFP), or both, andproduces images of the target compound(s) after removal of theautofluorescence signal. There are also mutant strains developed whichmay also be used. Any of these may be combined with the GFP when thatproduces useful results. Also, for examples, the target compound can bebased on quantum dots, which of size distributions tailored to providevarious spectral signatures.

In addition to a tunable liquid crystal spectral filter, otherembodiments for the spectral filtering are possible, including, forexample, an acousto-optical tunable spectral filter, a set of spectralfilters, a spectral filter wheel, a dispersive prism, a grating, aspectrometer, or monochromator. For example, one could use a motorizedfilter wheel containing a plurality of bandpass filters. Yet anotherembodiment could use a split-image system from Optical Insights (Tucson,Ariz.) to view the specimen in four spectral bands at once, albeit withlower spatial resolution. The bands are chosen to give a spectrum thatdistinguishes between the target compound and backgroundautofluorescence, i.e. to have cos θ that is significantly differentfrom 1, preferably 0.8 or less.

There are also numerous techniques for obtaining spectrally resolvedimages. For example, some of these techniques include: “Raster-scanning”systems where each pixel is illuminated and the emission spectrumacquired, and the image is obtained by sequentially acquiring eachpixel; “Push-broom” systems, where spectra are acquired from a line ofthe sample by taking the emission light from a line, or strip, of thesample, and putting it through an imaging grating to acquire a line ofspectra from the sample, and then the sample is moved along under theimaging system (or the imaging system is moved over the sample) andanother line is acquired until an image is built-up; and “True-imaging”systems, that take spectrally resolved images of a sample.

Furthermore, while the above techniques and analysis have focused onsituations in which the light coming from the sample is spectrallyfiltered to discriminate between selected components. The samealgorithms can be applied to the situation in which the light used toilluminate the sample is spectrally filtered to discriminate betweenselected components. In other words, rather than focus on the emissionspectra of the different components in the sample, one can focus on theexcitation spectra of the different components in the sample. In suchembodiments, the light measured from a particular region of the sampleis spectrally resolved as a function of different spectral weightingfunctions for the excitation light. The basic principle for spectralunmixing remains the same, however, because the intensity of themeasured light at for each spectral weighting function will includecontributions from different components according to the degree to whichthose components are excited by light at corresponding to that spectralweighting function. For example, FIG. 17 shows a spectral imaging system100′ that includes an excitation side spectral filter 1700 for switchingamong multiple excitation wavelength bands. The resulting image cubedata can be processed as described above where, for example, thewavelength λ in equation [1] is used to denote a given wavelength (orwavelength band) of excitation. The illumination source for suchtechnique may be a broadband source which is then spectrally filtered asabove, or an array of sources having different spectral emission, suchas an LED or diode array. Such techniques are also further described inU.S. application Ser. No. 10/226,592 (Publication No.US-2003-0081204-A1), which is incorporated herein by reference. Othertechniques for providing excitation light can also be used such as thosedescribed in U.S. application Ser. No. 10/163,233 (Publication No.US-2003-0223248-A1), incorporated herein by reference.

Furthermore, in some embodiments, data may be collected as a function ofboth the excitation and emission wavelengths.

Though the techniques were described above in terms of measuringfluorescence, the same techniques can be applied to determiningconcentration of one or more target compounds based on other types oflight measurements. The light emission phenomena can be based on any offluorescence, transmission, reflectance, scattering, Raman scattering,or phosphorescence. For example, nanoparticles available from NanoplexTechnologies Inc., (www.nanoplex.com) in Menlo Park, Calif. can be usedas target compounds that provide Raman emission. Some of thesemeasurements may include conversion of measured signals into otherunits. What is important is that the measurement provide spectralinformation useful to distinguish a desired signal corresponding to atarget compound from other signal components, and thus to improve themeasurement integrity and sensitivity.

For example, some embodiments involve the transmission of light throughan absorbing medium. Under such conditions, the intensity of measuredlight is reduced with an exponential dependence upon target compoundconcentration. However, a transform to “optical density” (e.g., byapplying a logarithm function) enables linear unmixing techniques. Suchoptical density techniques are further described in U.S. applicationSer. No. 10/226,592 (Publication No. US-2003-0081204-A1), incorporatedherein by reference. Other embodiments may include measurements based onbrightness ratios at several selected wavelengths or wavelength bands.

Embodiments for estimating pure spectra may also include unsupervisedtechniques such as cluster analysis. Cluster analysis identifies regionsof the sample that have a similar spectral response by clustering thespectra such that the differences in the intra-cluster spectralresponses are minimized, while simultaneously maximizing theinter-cluster differences between spectral responses. In thisimplementation, the results of a cluster analysis include, for eachcluster, the cluster centroid spectrum (viz., the weighted mean spectrumfor the cluster), and the corresponding cluster membership map (viz.,the spatial distribution of the cluster). Taken together, they answertwo commonly posed questions about spectroscopic imaging: where did thedifferent types of spectra occur, and what were the spectralcharacteristics of the spectra.

Once regions with specific components have been identified throughexploratory unsupervised analyses, the location of those components canbe more thoroughly investigated using a supervised classifier. Trainingset spectra used for the supervised classifier can be extracted fromregions identified by the unsupervised analyses as containing a specificcomponent, or they can be selected from the data set using previousknowledge of the composition of the sample. The supervised classifiercan then refine the segmentation of the image by locating regions whichhave spectral profiles matching the training spectra. Unsupervisedanalyses, combined with a supervised classifier, provide a means oflocating the constituent components without a priori knowledge of thenumber or nature of the components present in the sample. In addition,using the spectra selected for the training set as targets in libraryspectral search routines would also enable an automated identificationof the components.

Supervised pattern recognition methods are potentially better suited tothe development of clinically or industrially useful data analysismethods. Supervised pattern recognition techniques such as lineardiscriminant analysis (LDA) or neural networks make use of the fact thatthe investigator often has a substantial amount of spectroscopic dataavailable (either biochemical or clinical). For example, theinvestigator may know that spectra arise from well-defined samplecomponents or tissue types. This information may then be used to train aLDA algorithm to recognise the particular combinations of variables(peak frequencies, bandwidths, relative intensities, etc.) in thespectra that are characteristic of these sample components or tissuetypes.

The spectral analysis and construction of the sample image can beimplemented in hardware or software, or a combination of both. Theelectronic processing can be implemented in computer programs usingstandard programming techniques following the methods described herein.Program code is applied to input data to perform the spectral unmixingfunctions described herein and generate output information such as thesample image. The output information is applied to one or more outputdevices such as a display monitor.

Each program is preferably implemented in a high level procedural orobject oriented programming language to communicate with a computersystem. However, the programs can be implemented in assembly or machinelanguage, if desired. In any case, the language can be a compiled orinterpreted language. Moreover, the program can run on dedicatedintegrated circuits preprogrammed for that purpose.

Each such computer program is preferably stored on a storage medium ordevice (e.g., CD-ROM or magnetic diskette) readable by a general orspecial purpose programmable computer, for configuring and operating thecomputer when the storage media or device is read by the computer toperform the procedures described herein. The computer program can alsoreside in cache or main memory during program execution. For example,computer 180 in FIG. 1 may includes a processor, an input/output controlcard, a user interface, such as a keyboard and monitor, and a memory. Aprogram stored on a computer-readable medium is stored in the computermemory, and when executed, the program causes the processor to carry outthe steps of analyzing the spectrally filtered images.

Additional aspects, features, and advantages are within the scope of thefollowing claims.

1. A method for spectrally unmixing light data corresponding to light emitted from multiple light sources internal to an animal, the method comprising: selecting an excitation wavelength band for excitation light provided to the animal; selecting an emission wavelength band that limits light wavelengths collected from the animal; providing excitation light, into the animal, that is limited to the excitation wavelength band; capturing a first image of at least a portion of the animal, where the first image includes light data that is limited in wavelength to the emission wavelength band and corresponds to light emitted by multiple fluorescent light sources internal to the animal; changing the excitation wavelength band and/or the emission wavelength band; capturing at least one additional image of at least the portion of the animal that uses a different combination of excitation wavelength band and emission wavelength band for each additional image, where each additional image includes light data that corresponds to light emitted by the multiple fluorescent light sources internal to the animal and is limited in wavelength to the emission wavelength band of the different combination; and using an iterative solution process, unmixing spectra for the multiple fluorescent light sources internal to the animal to provide a spectrum for each light source.
 2. The method of claim 1 further comprising determining the number of fluorescent light sources before unmixing spectra for the multiple fluorescent light sources.
 3. The method of claim 1 wherein a total number of images captured by the camera is no less than the number of fluorescent light sources in the animal.
 4. The method of claim 1 wherein the different combination for each additional image uses a common excitation wavelength band and multiple emission wavelength bands.
 5. The method of claim 1 wherein the different combination for each additional image uses a common emission wavelength band and multiple excitation wavelength bands.
 6. The method of claim 1 where the excitation light is provided into the animal from a portion of the animal that is visible to the camera during image capture.
 7. The method of claim 1 where the excitation light is provided into the animal from a portion of the animal that is not visible to the camera during image capture.
 8. The method of claim 1 wherein the iterative solution process also outputs a spatial distribution map for each of the multiple fluorescent light sources internal to the animal, where each spatial distribution map provides a two dimensional representation of a distribution of a fluorescent light source in the animal.
 9. The method of claim 1 further comprising providing an initial estimate of each spectrum for the multiple fluorescent light sources.
 10. The method of claim 1 further comprising applying a constraint to the iterative solution process.
 11. The method of claim 10 wherein the constraint includes a spectral constraint.
 12. The method of claim 11 wherein the spectral constraint includes a non-negativity constraint that limits the spectrum for each light source to a non-negative number.
 13. The method of claim 11 wherein the spectral constraint includes a unimodality constraint that limits the spectrum for each light source to having a single peak.
 14. The method of claim 11 wherein the spectral constraint includes a bandpass constraint that limits spectral data input to the iterative solution process to within a desired wavelength range.
 15. The method of claim 10 wherein the constraint includes a spatial distribution map constraint.
 16. The method of claim 15 wherein the spatial distribution map constraint includes a region of interest constraint that limits spatial area for a fluorescent light source in a spatial distribution map produced by the iterative solution process.
 17. The method of claim 1 wherein the multiple fluorescent light sources include tissue autofluoresence in the animal.
 18. (canceled)
 19. Logic encoded in one or more tangible media for execution and, when executed, operable to spectrally unmix light data corresponding to light emitted from multiple light sources internal to an animal, the logic including: instructions for selecting an excitation wavelength band for excitation light provided to the animal; instructions for selecting an emission wavelength band that limits light wavelengths collected from the animal; instructions for providing excitation light, into the animal, that is limited to the excitation wavelength band; instructions for capturing a first image of at least a portion of the animal, where the first image includes light data that is limited in wavelength to the emission wavelength band and corresponds to light emitted by multiple fluorescent light sources internal to the animal; instructions for changing the excitation wavelength band and/or the emission wavelength band; instructions for capturing one or more additional images of at least the portion of the animal, using a different combination of excitation wavelength band and emission wavelength band for each additional image, where each additional image includes light data that corresponds to light emitted by the multiple fluorescent light sources internal to the animal and is limited in wavelength to the emission wavelength band of the different combination; and instructions for unmixing spectra, using an iterative solution process, for the multiple fluorescent light sources internal to the animal to provide a spectrum for each light source. 20.-24. (canceled)
 25. A method for spectrally unmixing light data corresponding to light emitted from multiple light sources internal to an animal, the method comprising: selecting an emission wavelength band that limits light wavelengths collected from the animal; capturing a first image of at least a portion of the animal, where the first image includes light data that is limited in wavelength to the emission wavelength band and corresponds to light emitted by the multiple light sources internal to the animal; changing the emission wavelength band; capturing one or more additional images of at least the portion of the animal, using a different emission wavelength band for each additional image, where each additional image includes light data that corresponds to light emitted by the multiple light sources internal to the animal and is limited in wavelength to the different emission wavelength band; and using an iterative solution process, unmixing spectra for the multiple light sources internal to the animal to provide a spectrum for each light source, wherein the iterative solution process implements a multivariate curve resolution and iteratively solves for a spectrum of each of the multiple light sources using finishing criteria.
 26. The method of claim 25 wherein the multiple light sources include a bioluminscent light source and tissue autofluoresence in the animal.
 27. The method of claim 25 further comprising selecting an excitation spectrum for excitation light provided to the animal and providing excitation light, into the animal, that is limited to the excitation wavelength band, when the multiple light sources include a fluorescent light source.
 28. The method of claim 27 wherein the iterative solution process operates simultaneously on images for a) multiple illumination positions, b) multiple combinations of excitation wavelength bands, and c) multiple emission wavelength bands.
 29. The method of claim 25 wherein the iterative solution process also outputs a spatial distribution map for each of the multiple light sources internal to the animal, where each spatial distribution map provides a two dimensional representation of a distribution of a light source in the animal.
 30. The method of claim 25 wherein the finishing criteria uses an alternating least square method.
 31. An imaging system for imaging an animal, the imaging system comprising: an imaging chamber that includes a set of walls and a door that enclose an interior cavity, a stage configured to support the animal within the interior cavity, a camera, and a fluorescent excitation source; and a processing system including a processor and memory, the memory including instructions for selecting an excitation wavelength band for excitation light provided to the animal, instructions for selecting an emission wavelength band that limits light spectra collected from the animal, instructions for obtaining one or more fluorescent images using the camera, and instructions for unmixing spectra, using an iterative solution process, for the multiple fluorescent light sources internal to the animal to provide a spectrum for each fluorescent light source.
 32. The imaging system of claim 31 further comprising: an excitation filter configured to intercept excitation light produced by the fluorescent excitation source before the excitation light reaches the animal; and an emission filter configured to intercept emission light emitted from the animal before the emission light reaches the camera.
 33. The imaging system of claim 31 further comprising a structured light projector.
 34. The imaging system of claim 31 wherein the interior cavity is substantially light tight.
 35. The imaging system of claim 31 wherein the fluorescent excitation source is below the stage. 